scholarly journals Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

2021 ◽  
Author(s):  
Suryadipto Sarkar ◽  
Teresa Wu ◽  
Matthew Harwood ◽  
Alvin Silva
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Lymph Nodes ◽  
Prostate Gland ◽  
Hybrid Approach ◽  
The United States ◽  
Learning Classifier ◽  
Malignant Lymph Nodes ◽  
Slow Growing ◽  
Deep Learning Model

Abstract Prostate cancer is the second most common new cancer diagnosis in the United States. The prostate gland sits beneath the urinary bladder and surrounds the first part of the urethra. Usually, prostate cancer is slow-growing; stays confined to the prostate gland; and can be treated conservatively (active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that suggests the cancer is more aggressive and surgery is not adequate. In those cases, radiation and/or systemic therapies (e.g., chemotherapy, immunotherapy) are required. The challenge is that it is often difficult for radiologists to differentiate malignant lymph nodes from non-malignant ones with current medical imaging technology. In this study, we design a scalable hybrid approach utilizing a deep learning model to extract features into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer.

Screening patents of ICT in construction using deep learning and NLP techniques

2020 ◽  
Vol 27 (8) ◽  
pp. 1891-1912
Author(s):  
Hengqin Wu ◽  
Geoffrey Shen ◽  
Xue Lin ◽  
Minglei Li ◽  
Boyu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fundamental Problem ◽  
The United States ◽  
Learning Model ◽  
Content Type ◽  
Patent Retrieval ◽  
Part Of Speech ◽  
Textual Data ◽  
Deep Learning Model

PurposeThis study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.Design/methodology/approachThis study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.FindingsThe validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.Practical implicationsThis study contributes a specific collection for ICTC patents, which is not provided by the patent offices.Social implicationsThe proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.Originality/valueA deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.

Arabic tweets sentiment analysis – a hybrid scheme

2016 ◽  
Vol 42 (6) ◽  
pp. 782-797 ◽  
Author(s):  
Haifa K. Aldayel ◽  
Aqil M. Azmi
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Hybrid Approach ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Good Source ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Semantic Orientation ◽  
F Measure

The fact that people freely express their opinions and ideas in no more than 140 characters makes Twitter one of the most prevalent social networking websites in the world. Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public’s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present – using Saudi Arabia as a basis – we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach that combines semantic orientation and machine learning techniques. Through this approach, the lexical-based classifier will label the training data, a time-consuming task often prepared manually. The output of the lexical classifier will be used as training data for the SVM machine learning classifier. The experiments show that our hybrid approach improved the F-measure of the lexical classifier by 5.76% while the accuracy jumped by 16.41%, achieving an overall F-measure and accuracy of 84 and 84.01% respectively.

The 4Kscore to predict the grade and stage of prostate cancer in the radical prostatectomy specimen: Results from a multi-institutional prospective trial.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 69-69
Author(s):  
Bruno Nahar ◽  
Sanoj Punnen ◽  
Daniel Sjoberg ◽  
Stephen M Zappala ◽  
Dipen Parekh
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Validation Study ◽  
Prostate Biopsy ◽  
Prostate Gland ◽  
Prospective Trial ◽  
Digital Rectal Examination ◽  
Treatment Decision ◽  
The United States ◽  
Surgical Specimen

69 Background: A recent prospective validation study confirmed the 4Kscore accurately predicted aggressive prostate cancer on prostate biopsy. We investigated the association between the 4Kscore and pathologic grade and stage at radical prostatectomy, where the entire prostate gland is sampled. Methods: Prospective enrollment of 1,312 men who were referred for prostate biopsy for clinical suspicion of prostate cancer occurred at 26 sites throughout the United States from October 2013 to April 2014. We selected men who were found to have positive prostate cancer biopsies and elected to undergo radical prostatectomy. The 4Kscore is an algorithm that incorporates a panel of 4 Kallikreins (total PSA, free PSA, intact PSA and human kallikrein-2) in addition to age, digital rectal examination, and prior biopsy status. We assessed the concordance between the 4Kscore prior to biopsy and grade of prostate cancer at radical prostatectomy. 4Kscore test results were compared for those with and without non organ-confined tumors at surgery using the Wilcoxon rank-sum. Results: Among the 1,312 men who enrolled in this validation study, 144 were found to have prostate cancer and underwent radical prostatectomy. We saw a significant association between the 4Kscore and grade at surgery with higher scores relating to worse grade. For men with Gleason 6, 7, and 8 or higher cancers in the surgical specimen the median (IQR) 4Kscore was 7% (4, 12), 25% (12, 38), and 47% (24, 66) (p<0.0001).The median 4Kscore among men with non organ-confined cancer was significantly higher then men with cancers confined to the prostate ([36% (IQR 19,58)] vs. [19% (IQR 9, 35)], p=0.002). Conclusions: In a subset of men who underwent radical prostatectomy, the 4Kscore was significantly associated with pathological grade and extracapsular extension in the surgical specimen, with higher scores being linked to higher grade and more aggressive histology. The test can be beneficial to aid in treatment decision making for men who are contemplating observation of their cancer versus immediate treatment.

Benchmarking the Performance of a Machine Learning Classifier Enabled Multiobjective Genetic Algorithm on Six Standard Test Functions

2017 ◽  
Author(s):  
Kayla Zeliff ◽  
Walter Bennette ◽  
Scott Ferguson
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Objective Function ◽  
Hybrid Approach ◽  
Pareto Frontier ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multiobjective Genetic Algorithm ◽  
Learning Classifier ◽  
Function Calls

Previous work tested a multi-objective genetic algorithm that was integrated with a machine learning classifier to reduce the number of objective function calls. Four machine learning classifiers and a baseline “No Classifier” option were evaluated. Using a machine learning classifier to create a hybrid multiobjective genetic algorithm reduced objective function calls by 75–85% depending on the classifier used. This work expands the analysis of algorithm performance by considering six standard benchmark problems from the literature. The problems are designed to test the ability of the algorithm to identify the Pareto frontier and maintain population diversity. Results indicate a tradeoff between the objectives of Pareto frontier identification and solution diversity. The “No Classifier” baseline multiobjective genetic algorithm produces the frontier with the closest proximity to the true frontier while a classifier option provides the greatest diversity when the number of generations is fixed. However, there is a significant reduction in computational expense as the number of objective function calls required is significantly reduced, highlighting the advantage of this hybrid approach.

scholarly journals Dual-Phase PET-CT to Differentiate [18F]Fluoromethylcholine Uptake in Reactive and Malignant Lymph Nodes in Patients with Prostate Cancer

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e48430 ◽  
Author(s):  
Daniela E. Oprea-Lager ◽  
Andrew D. Vincent ◽  
Reindert J. A. van Moorselaar ◽  
Winald R. Gerritsen ◽  
Alfons J. M. van den Eertwegh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lymph Nodes ◽  
Dual Phase ◽  
Pet Ct ◽  
Malignant Lymph Nodes

scholarly journals Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry

2021 ◽  
Author(s):  
Joshua Levy ◽  
Christopher M Navas ◽  
Joan A Chandra ◽  
Brock Christensen ◽  
Louis J Vaickus ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Anorectal Manometry ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Learning Model ◽  
Anorectal Function ◽  
High Definition ◽  
Deep Learning Model

BACKGROUND AND AIMS: Evaluation for dyssynergia is the most common reason that gastroenterologists refer patients for anorectal manometry, because dyssynergia is amenable to biofeedback by physical therapists. High-definition anorectal manometry (3D-HDAM) is a promising technology to evaluate anorectal physiology, but adoption remains limited by its sheer complexity. We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia. METHODS: Spatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018-2020 at a tertiary institution. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity. RESULTS: 302 3D-HDAM studies representing 1,208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy (AUC=0.91 [95% confidence interval 0.89-0.93]) to traditional (AUC=0.93[0.92-0.95]) and hybrid (AUC=0.96[0.94-0.97]) predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive (odds ratio=4.21[2.78-6.38]) versus traditional/hybrid approaches. CONCLUSIONS: By considering complex spatial-temporal information beyond conventional anorectal function metrics, deep learning on 3D-HDAM technology may enable gastroenterologists to reliably identify and manage dyssynergia in broader practice.

scholarly journals Prostate Cancer in Lebanon: Incidence, Temporal Trends, and Comparison to Countries From Different Regions in the World

2021 ◽  
Vol 28 ◽  
pp. 107327482110552
Author(s):  
Najla A. Lakkis ◽  
Mona H. Osman
Keyword(s):  
Prostate Cancer ◽  
Incidence Rate ◽  
Task Force ◽  
Public Awareness ◽  
Temporal Trends ◽  
The United States ◽  
Incidence Rates ◽  
P Value ◽  
The World ◽  
Slow Growing

Background Prostate cancer is the most common malignancy in men globally. This study aims at investigating the incidence rates and trends of prostate cancer in Lebanon, and to compare them to those of countries from different regions in the world. Methods Data on prostate cancer were obtained from the Lebanese national cancer registry for the years 2005 to 2016. The calculated age-standardized incidence and age-specific rates were expressed as per 100 000 population. Results In Lebanon, prostate cancer is ranked as the most common cancer in men. The age-standardized incidence rate of prostate cancer has increased from 29.1 per 100 000 in 2005 to 37.3 per 100 000 in 2016; the highest rate was in 2012, surpassing the global average incidence rate for that year. The age-specific incidence rate of prostate cancer has increased exponentially starting at the age of 50 years to reach its peak in men aged 75 years or more. Two trends were identified in the age-standardized incidence rate of prostate cancer; an average significant increase of 7.28% per year for the period 2005–2009 ( P-value < .05), followed by a non-significant decrease of around .99% for the period between 2009 and 2016 ( P-value > .05). The age-standardized incidence rate in Lebanon was higher than most countries in the Middle East and North Africa region and Asia, but lower than the rates reported in Australia, America, and different European countries. Conclusion Prostate cancer is the leading cancer among men in Lebanon. Screening practices, changes in population age structure, and prevalence of genetic and risky lifestyle factors may explain the increased incidence rates of prostate cancer. Given the controversy of screening recommendations and the slow growing nature of prostate cancer, increasing public awareness on ways of prevention, and implementing the latest screening recommendation of the United States Preventive Services Task Force are the suggested way forward.

scholarly journals Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1767 ◽  
Author(s):  
Piotr Woźnicki ◽  
Niklas Westhoff ◽  
Thomas Huber ◽  
Philipp Riffel ◽  
Matthias F. Froelich ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Prostate Gland ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Ability ◽  
Image Data ◽  
Learning Models ◽  
Potential Biomarker ◽  
Machine Learning Models

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

Sign in / Sign up

Export Citation Format

Share Document